Teachers’ authentic strategies to support student motivation
Bibliographic record
Abstract
Introduction Most theories of motivation have largely developed from the work of scholars rather than the perspectives of teachers. This means that although researchers have many recommendations to guide the way teachers motivate students, there is little understanding of what teachers naturally do to support student motivation. The purpose of this study was to prioritize teachers’ perspectives by asking them, separate from theory, what they do to motivate students. Methods Forty-two practicing teachers completed an open-ended online survey in which they described their personal strategies for motivating students. We used thematic analysis to identify codes and themes from practicing teachers’ responses in a qualitative descriptive design. Results We identified 36 discrete codes that gave rise to nine themes: relevance, interest, relationships, effort, safe environment, goals, student self-regulated learning, delivery, and rewards. Member checks were completed to provide evidence of confidence in the results. Discussion All of the strategies that teachers described align with recommendations motivation researchers would make with the exception of rewards, which, from a research perspective, are often discouraged. We discuss the results in light of motivation design principles and their relevance to partnering with teachers as a ubiquitous influence on student motivation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".